Publications

The opinions and conclusions expressed in these papers are those of the authors and do not necessarily represent those of the Federal Reserve Bank of Chicago, Federal Reserve Board of Governors or the University of Illinois.

Click on the categories below to view publications.

Papers

2018

Abstract: Extended input–output models require careful estimation of disaggregated consumption by households and comparable sources of labor income by sector. The latter components most often have to be estimated. The primary focus of this paper is to produce labor demand disaggregated by workers’ age. The results are evaluated through considerations of its consistency with a static labor demand model restricted with theoretical requirements. A Bayesian approach is used for more straightforward imposition of regularity conditions. The Bayesian model confirms elastic labor demand for youth workers, which is consistent with what past studies find. Additionally, to explore the effects of changes in age structure on a regional economy, the estimated age-group-specific labor demand model is integrated into a regional input–output model. The integrated model suggests that ceteris paribus ageing population contributes to lowering aggregate economic multipliers due to the rapidly growing number of elderly workers who earn less than younger workers.

Abstract: Distance to the CBD and neighboring commercial employment (land-use) have been the core determinants of spatial-related production externalities for firms. In these models, travel time to work (firms) is the single most important factor for residential land-use allocation. New theories of complex urban systems (CUS), however, have begun to cast some doubt on the efficacy of the “distance to CBD” model. There is some evidence for example, that large urban systems might evolve with scale-free transportation networks. In this paper, we examine urban land-use data from the Chicago Metropolitan Statistical Area (MSA) to argue for a theoretical shift from a “distance to CBD” based prototype to one that considers the complexity inherent in urban systems structure. We use a Stochastic Greedy Algorithm to quantify connectivity and attractions to every land cell (30 × 30 m) to existing population and employment centers, Points of Interests (POIs), and highway and major roads. We measure the frequency of commercial and resident land-uses relative to these found attraction levels and develop algorithms that help explain the relations. Using these methods, we find that both CBD-driven and network-driven approaches are empirically valid for explaining current urban structures. We also find, however, that these relations change when temporal variables are considered. For example, we found that the land-use change in Chicago from 2001 to 2011 is an obvious deviation from the “distance to CBD” based urban growth assumption. Our results suggest that we should re-examine the core urban structure assumptions of spatial equilibrium models.

Abstract: Median-based Housing Price Indices (HPIs) generate potentially misleading indicators especially when applied to Small Spatial Units (SSUs) such as small Metropolitan Statistical Areas (MSAs) or neighborhoods within larger metropolitan regions. Given the small total number of sales in SSUs and the even smaller number of repeat sales, traditional alternatives are few. A matching-based Fisher HPI is proposed as an alternative that provides more accurate estimations for SSUs given that it both controls for housing characteristics and is not restricted just to repeated sales. This paper makes use of housing sales data from the state of Illinois, as an example of the application of the methodology to small MSAs and at the neighborhood level within larger MSAs. The results highlight some of the biases that have arisen from the use of median price indicators.

Abstract: Due to the concentration of assets in disaster‐prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single‐region input–output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input–output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.

Abstract: Growing world population and the uncertain hazards that accompany climate change put an increasing pressure on the management and sustainability of scarce environmental resources, notably water. In spite of its water scarcity, the state of Arizona permits as much as 73% of its water to be consumed by a single sector, crop production. Since 79% of such crop production is not consumed in Arizona, it corresponds to exporting up to 67% of the water available in the state to the rest of the country and abroad. It has certain and glooming consequences on the availability of water for a state expected to see its population grow and its climate get drier. Based on input-output techniques, we simulate three scenarios aiming at saving 19% of the water available, a figure set by the first of them based on improving the efficiency of the current irrigation system. The same savings could also be reached by a twenty-seven-fold increase in the price of water or a 19.5% reduction in crop exports. Estimates indicate that the least costly solution is a more efficient irrigation system while export reduction is the second-best choice.

Abstract: Atmospheric rivers (ARs) account for more than 75 % of heavy precipitation events and nearly all of the extreme flooding events along the Olympic Mountains and western Cascade Mountains of western Washington state. In a warmer climate, ARs in this region are projected to become more frequent and intense, primarily due to increases in atmospheric water vapor. However, it is unclear how the changes in water vapor transport will affect regional flooding and associated economic impacts. In this work we present an integrated modeling system to quantify the atmospheric–hydrologic–hydraulic and economic impacts of the December 2007 AR event that impacted the Chehalis River basin in western Washington. We use the modeling system to project impacts under a hypothetical scenario in which the same December 2007 event occurs in a warmer climate. This method allows us to incorporate different types of uncertainty, including (a) alternative future radiative forcings, (b) different responses of the climate system to future radiative forcings and (c) different responses of the surface hydrologic system. In the warming scenario, AR integrated vapor transport increases; however, these changes do not translate into generalized increases in precipitation throughout the basin. The changes in precipitation translate into spatially heterogeneous changes in sub-basin runoff and increased streamflow along the entire Chehalis main stem. Economic losses due to stock damages increase moderately, but losses in terms of business interruption are significant. Our integrated modeling tool provides communities in the Chehalis region with a range of possible future physical and economic impacts associated with AR flooding.

2017

Abstract: The Neighborhood Stabilization Program (NSP) is a $7 billion nationwide government program that was established to reduce the negative impacts of the housing crisis in foreclosure-concentrated neighborhoods. NSP rehabilitations aim to bring foreclosed and abandoned properties back to productive use. Very few quantitative studies have evaluated NSP and provided policy suggestions for future stabilization. Furthermore, there is some ambiguity about the channels through which foreclosures influence neighboring properties. This study fills the gap in the literature by evaluating the effects of NSP acquisition and rehabilitation in terms of the impact on elevating neighboring property values. In addition, it provides evidence that disamenity effects are a source of the negative impacts of foreclosures on their neighbors. Using a 2008–2014 repeated cross-section dataset for housing sales in the city of Chicago, the difference-in-differences estimates reveal that the average sales prices of homes within 0.1 miles of the NSP projects increased by 14.3% and these effects do not appear until the completion of the rehabilitation. Furthermore, large program effects are found for normal homes but not for foreclosure-related homes. The results vary under different contexts of NSP implementation, but the analytical approach presented in this study is reproducible for NSP studies in other regions.

Abstract: This study investigates the potential role of variations in technical efficiency as a contributing factor in providing an explanation for convergence or divergence in Western Europe. To control for spatial dependence among regions, it uses a spatial stochastic frontier framework that integrates spatial econometric techniques with stochastic frontier panel-data models. The empirical analysis reveals there is a strong geographical pattern of regional efficiency, while the degree of average regional efficiency has increased steadily year by year. From a European regional policy perspective, we can conclude that differentiated development strategies seem more appropriate than homogeneous or best-practice investment approaches.

Abstract: Despite regulatory measures restricting industrial and agricultural operations from pouring pollutants into lakes, streams, and rivers, around 1.1 trillion m3 of wastewater were still disposed in waterways around the world in 2009 and this amount continues to grow. Several studies have analyzed the determinants of gray water increase at local level, but so far none has explored them in an international context to highlight the role of global value added chains and the dichotomy between developed and developing nations. In order to provide insights on the dynamics of water pollution worldwide, this paper analyzes the main drivers of gray water discharge during the 1995–2009 period and the effort on reducing gray water compared to other environmental externalities. Based on the World Input-Output Database, a structural decomposition analysis (SDA) of gray water generation shows that domestic demand for food is the main driver of gray water changes in all countries, while relocation of industrial activities to developing nations has disproportionally transferred this burden from developed countries' manufacturing industries. We highlight that while national policies should target water pollution from the agri-food sector within each country, water discharges from manufacturing sectors in global value chains need to be regulated from an international perspective. Besides the empirical evidence on water pollution drivers currently lacking in the environmental literature, this paper also introduces a novel hybrid multiplicative-additive SDA that overcomes the issue of distributing large interaction terms in standard additive models and allows simulating mitigation scenarios. It portrays the heterogeneity among sectors of how environmental abatement policies affect water and air pollution.

Abstract: The input–output framework has evolved dramatically since its initial formulation. New analytical techniques and extensions have allowed a more comprehensive assessment of the economy and expanded its applicability. Nonetheless, the core of the framework has remained unchanged: an annually compiled input–output table, which conveys monetary flows between sectors in a region in a particular year. Hence, the technical coefficients derived from it are ‘average’ input compositions, neglecting fluctuations in production capacity, seasonality and temporal shocks within that period. This paper develops a consistent methodology to disaggregate the annual input–output table in its time dimension in order to estimate intra-year input–output matrices with distinct technical structures for a particular year. The main advantages in relation to the annual model are to allow seasonal effects to be studied within the input–output framework, to better understand the process of coefficient change and to offer a more comprehensive dynamic view of production.

Abstract: The estimation of the impact of public investment on regional economic growth requires consideration of the spatio-temporal dynamics among the state variables of each region. Recent austerity policies in Spain that feature temporary decreases in the accumulation of regional public capital should thus be evaluated in terms of their impact on the economy as a whole, on specific regions together with the spillovers effects from one region to the rest of the regional system. Applying a multiregional integrated specification to model interdependencies across regions, our results indicate that, while global decreases in public investment have a homogenously negative effect on the output of all the regions, the Spanish regions portray heterogeneous responses from localized public capital stock reductions over the simulation period considered.

Abstract: Meta-analysis of the impact of European Union Structural Funds on regional growth. Regional Studies. This paper offers a meta-regression analysis of the controversial impact of European Union Structural Funds on the growth of the recipient regions. It identifies the factors that explain the heterogeneity in the size of 323 estimates of their impact recorded in 17 econometric studies. Heterogeneity comes from the publication status, the period examined, the control of endogeneity and the presence of several regressors, but not from differences in functional forms.

2016

Abstract: Access to high quality spatial data raises fundamental questions about how to select the appropriate scale and unit of analysis. Studies that evaluate the impact of conservation programs have used multiple scales and areal units: from 5x5 km grids; to 30m pixels; to irregular units based on land uses or political boundaries. These choices affect the estimate of program impact. The bias associated with scale and unit selection is a part of a well-known dilemma called the modifiable areal unit problem (MAUP). We introduce this dilemma to the literature on impact evaluation and then explore the tradeoffs made when choosing different areal units. To illustrate the consequences of the MAUP, we begin by examining the effect of scale selection when evaluating a protected area in Mexico using real data. We then develop a Monte Carlo experiment that simulates a conservation intervention. We find that estimates of treatment effects and variable coefficients are only accurate under restrictive circumstances. Under more realistic conditions, we find biased estimates associated with scale choices that are both too large or too small relative to the data generating process or decision unit. In our context, the MAUP may reflect an errors in variables problem, where imprecise measures of the independent variables will bias the coefficient estimates toward zero. This problem may be pronounced at small scales of analysis. Aggregation may reduce this bias for continuous variables, but aggregation exacerbates bias when using a discrete measure of treatment. While we do not find a solution to these issues, even though treatment effects are generally underestimated. We conclude with suggestions on how researchers might navigate their choice of scale and aerial unit when evaluating conservation policies.

Kang, D., & Dall'erba, S. (2016). An Examination of the Role of Local and Distant Knowledge Spillovers on the US Regional Knowledge Creation. International Regional Science Review, 39(4), 355-385.

Abstract: This article examines the role of academic and private R&D spending in the frame of a knowledge production function estimated across 3,109 US counties. We distinguish the role of local, face-to-face, knowledge spillovers that are determined by geographical proximity from distant spillovers captured by a matrix of patent creation–citation flows. The advantage of the latter matrix is its capacity to capture the direction of the spillovers. We control for the spatial heterogeneity between metropolitan and nonmetropolitan counties as well as between states. Our empirical results show that spillovers due to private knowledge contribute to higher returns in metropolitan counties than in nonmetropolitan regions. On the other hand, knowledge created in the academia leads to spillovers displaying spatially homogeneous returns. Our results imply that future innovation policies need to grasp more fully the role of distant knowledge spillovers, especially those generated in the academia, and recognize better the presence of heterogeneity in the sources and location of knowledge creation.

Abstract: As an increasingly adopted renewable energy resource, solar power has a high potential for carbon emission reduction and economic development. This paper calculates the impact on job, income and output creation of a new solar power plant in an input‐output framework. The contribution is twofold. First, we compare the multipliers generated by the construction and operation/maintenance of a plant located in California with those it would have generated had it been built in Arizona. Second, we point out the differences in the results obtained with the popular IMPLAN software from those we get with the solar photovoltaic model of JEDI.

Abstract: This paper estimates a Ricardian model of farmland value across the counties of the semiarid Southwestern United States. Compared to previous contributions, we focus on one climate zone and include the presence of extreme weather events and of farm subsidies in our analysis. We also control for heterogeneity and for various types of spillover effects. Once calibrated, the model is used to project changes due to future climate conditions. We find that the probability of a decrease is great in highland counties while an increase or decrease is equally probable in lowland counties where climate impacts farmland value less.

Abstract: Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.